The Digital Product Passport (DPP) is coming. And for many manufacturers, it initially sounds like nothing more than a cost driver: labels, software, data maintenance – additional expenses required by regulation.

But seeing the DPP only as a compliance obligation means missing out on major potential.

TL;DR

• The DPP scan gives you a legitimate, high-intent touchpoint directly on the product—and can both reduce costs and increase revenue.
• Three practical playbooks show where the value comes from: Fix (help customers solve issues), Buy (recommend compatible parts and accessories), and Book (drive service appointments).
• Optimize based on value per scan and track repeat scans.
• Keep revenue-driving data separate from compliance data, but make both easily accessible.
• Start with a small pilot: 10 SKUs, one playbook, two QR placements, and measure results for 30 days.
• Update content regularly and visibly to encourage repeat scans
  

The Digital Product Passport: Cost Driver or New Revenue Stream?

Yes, the Digital Product Passport costs money. In practice, most expenses fall in the lower cent range per product. QR codes cost around €0.10 to €0.15; NFC tags around €0.25 to €0.30. Including platform and license costs, you typically end up around €0.40 per device at typical volumes.

But once you place a QR code on the product, you’ve already fulfilled three of the four key conditions for effective user activation:

• Right audience: reaching the person who actually uses the product
• Right place: putting it directly on the product
• Right time: producing the right information during use or when something goes wrong

The missing ingredient is: useful, action-oriented content.

And this is where it’s decided whether a scan delivers only compliance or real business impact.

Where Does Revenue Come From and How Do We Measure It?

Every scan can create value through three different mechanisms:

1. Fix:

The customer solves a problem without contacting support.
This lowers tickets, returns, and call center volume.
Measured by:

• Ticket deflection
• First-Time-Fix (FTF)
• Mean Time to Resolve (MTTR)

Even a modest deflection rate around 15% can have a large financial impact.

2. Buy:

After scanning, users see accessories or spare parts that are guaranteed to fit their exact model. This removes uncertainty and reduces returns.
Measured by:

• Scan-to-cart rate
• Contribution margin
• Accessory AOV

3. Book:

The scan leads directly to a service booking like maintenance, installation, upgrade and becomes more predictable

Measured by:

• Scan-to-book rate 
• No-show rate 
• Service AOV.

The Key Metric: Value per Scan

Formula:
(Accessory revenue + service revenue + avoided support costs) ÷ number of scans

Example

• 1,000 scans × 15% deflection × €12 per ticket = €1,800
  
• 1,000 scans × 3% scan-to-cart × €50 AOV × 40% margin = €600
  
• 1,000 scans × 1% scan-to-book × €120 × 50% margin = €600
  

→ €3,000 total value/month → €3 per scan

Best Ways to Utilize the Digital Product Passport (DPP)

Playbook A: Fix it fast

Many companies make it unnecessarily hard for users to get help. Hidden contact forms, overloaded hotlines, generic chatbots …

The DPP scan turns that around by offering immediate, product-specific help right where the problem occurs.

How it works

• The scan opens a page dedicated to that exact product.
• At the top: a clear button such as “Solve the issue”.
• A short, guided flow with images or 10-second videos leads users toward a solution.
• If the customer doesn’t contact support within 48 hours, it’s counted as successful deflection.
• If they do need help, support receives pre-filled details such as serial number or error codes.

Why it works

The scan combines high intent, context, and trust.
Users want a quick fix and are willing to follow short, targeted instructions.
According to Zendesk CX Trends 2024, 51% of users prefer self-service for urgent issues.

Key KPIs

• Ticket deflection
• First Time Fix (FTF) rate
• Mean Time to Repair (MTTR)
• Repeat scan rate
  

Pro tip:
If you can identify the issue clearly, show the relevant replacement part at the end to turn a Fix flow into a Buy opportunity.

Playbook B: Sell accessories & spare parts

Not every purchase has to go through Amazon. Context is a conversion driver and context is automatic in a DPP scan.

Instead of browsing a generic shop, users see 3–5 highly relevant suggestions: Required accessories, wear parts, safety upgrades, etc.

How it works

• Show a small, curated selection (3–5 items).
• Highlight compatibility clearly (“Guaranteed fit for [model]”).
• Provide a straightforward “Order now” button.
• Offer optional bundles (e.g., filter + seal + cleaning spray).

Why it works

Confidence in compatibility significantly boosts conversion rates and reduces returns.
It also keeps customers in your ecosystem rather than pushing them toward Amazon or third-party sellers.

Key KPIs

• Scan-to-cart rate
• Parts Average Order Value (AOV)
• Margin per scan
• Return rate

Playbook C: Sell service

Some products only deliver full value with proper installation, calibration, or regular maintenance.

The DPP scan lets you surface these needs at exactly the right moment—e.g., “Maintenance due in 12 days” or “Safety recall active.”

How it works

• The CTA leads to a booking page with real-time appointment slots.
• Clear pricing and service levels reduce hesitation.
• Automated reminders help prevent no-shows.
• After completion, service results can be logged in the DPP.

Why it works

It converts unpredictable, reactive service requests into predictable, recurring revenue.

Key KPIs

• Scan-to-book rate
• No-show rate
• Service AOV
• On-site First-Time-Fix rate

Pro tip:
Offer only three scheduling options:
“Later today”, “Tomorrow morning”, or “Choose a date.”
More choice slows people down.

Revenue Data vs. Compliance Data

Revenue-driving data includes:

• Compatibility
• Accessory recommendations
• Bundles
• Wear parts
• Warranty
• Recall status

This information changes frequently and is ideal for A/B tests and optimization.

Compliance-related data includes:

• Product and material information
• Serial/batch numbers
• Ownership history
• Repair and maintenance logs
• Sustainability details

This data must be consistent, traceable, and auditable.

Why separate them?

• Revenue data needs fast updates.
• Compliance data needs control and stability.
• Ownership lives in different teams:
  • Marketing/Product/Service → revenue
  • Regulatory/Quality/Legal → compliance

A good DPP experience uses revenue data to guide action, while compliance data stays accessible in the background.

QR Placement: Where Should the Code Live?

General guidance

• Packaging: Great for onboarding and pre-purchase info.
• Sticker: Flexible placement; useful when aesthetics matter.
• Point of sale: Helps with in-store comparison.
• On the device: Best for encouraging repeat scans.

Recommendation

Use at least two placements.
Packaging gets thrown away; POS displays disappear; the device remains.

Important Note

• Static content leads to one-time scans.
• Updated content leads to repeat scans.

Show updates clearly with timestamps (“Updated 3 days ago”) to build a scanning habit.

How to Organize Internally

A scan project touches nearly all central functions. Assigning clear roles like the ones outlined below helps to avoid bottlenecks:

• PIM/Data quality: Product management
• UX/Conversion: Marketing & Customer Success
• Service processes: Support
• IT/Security: Access, signatures, monitoring
  

Pro-tip:
Form a cross-functional DPP task force with shared KPIs such as scan rate, deflection, and scan-to-cart rate. 

Common Pitfalls—and How to Avoid Them

“Everything for everyone”

A single flow for customers, technicians, and retailers serves no one. Choose one persona and one primary goal per scan.

Long texts

PDFs on mobile kill conversions. Use snackable steps, max three actions per step, plus GIFs/short videos (10–15 s).

No attribution

Track with UTMs and event logs: page views, help start/completion, cart, booking confirmation.

The Next 5 Steps Starting Today

1. Set up DPP MVP fields in your PIM

Include mandatory fields plus revenue fields like compatibility, accessories, wear parts, warranty, recall status, error codes, media links, CTA text.

2. Choose SKUs for the pilot

Prioritize high-ticket or high-accessory products.

3. Create a landing template

Different versions for customer, technician, retailer—each with exactly one main action (Fix, Buy, or Book).

4. Test QR codes in 3 locations

Packaging, device, POS—measure for several weeks.

5. Build a dashboard with 6 KPIs

Scan rate, FTF, deflection, scan-to-cart, scan-to-book, value per scan.
Add a public goal (e.g., “value/scan ≥ €3.00 within 30 days”).

Conclusion

The DPP may be mandatory, but it’s also one of the most powerful product touchpoints available.

Use it to reduce support costs, sell the right parts at the right time, and turn service into predictable revenue.

The magic word is context.
If the scan delivers the exact help a customer needs in that moment, you earn trust and conversion.

Start small, measure value per scan, and scale what works.

PIM provides the data, PX provides the context, and the DPP opens the door.
What you do next is execution.

Note on QR/GS1 Digital Link & Exit Option

Products can use standard GS1 Digital Link (QR/NFC). You can change the target of the link at any time – homepage, promotions, manuals—without recalling or relabeling products. Only requirement: mandatory DPP information must remain accessible.

This article does not replace legal advice. Only the current delegating acts and sector-specific regulations apply.

It’s been about two weeks since the team here at Akeneo announced a new Accelerator for SAP S/4HANA during our Autumn Release, and we felt it deserved its own call-out. Why? Because nearly half of Akeneo’s customers who use an ERP rely on SAP. 

That makes this announcement both exciting and essential for enterprises seeking a faster, more flexible, and future-proof way to synchronize product information between Akeneo Product Cloud and SAP S/4HANA.

And the timing couldn’t be better. As Digital Product Passport (DPP) requirements come into effect in 2026, we’re entering an era where product data must be richer, traceable, and more transparent than ever before.

That means that product data can’t simply flow one way anymore. It must move in both directions, from suppliers and manufacturers into the PIM (Product Information Management system) and then back out to consumers, partners, and regulators. Businesses using SAP and Akeneo together will be better prepared to meet these requirements, and our new Accelerator is designed precisely to make that happen. 

What is SAP S/4HANA?

SAP S/4HANA is SAP’s modern enterprise resource planning (ERP) suite, designed to run on a real-time, cloud-based architecture. It’s where many of the world’s largest and most complex organizations manage their core business operations, everything from finance and logistics to product and pricing data.

In this ecosystem, Akeneo PIM plays a complementary role. While SAP serves as the backbone for operational and transactional data, Akeneo empowers teams to enrich, organize, and distribute product information across every customer-facing channel, from eCommerce platforms and mobile apps to print catalogs and in-store experiences.

Together, SAP S/4HANA and Akeneo PIM create a bridge between operational efficiency and customer experience excellence. And now, with the new Accelerator, that bridge just got stronger.

What is the Akeneo SAP S/4HANA Accelerator?

The Akeneo SAP S/4HANA Accelerator is designed to make connecting SAP and Akeneo faster, more flexible, and easier to maintain than ever before.

In the past, Akeneo’s SAP integrations followed a recurring model: we owned and maintained the connection, and customers relied on us for every update or change. While this approach worked, it created dependencies that slowed down innovation and limited flexibility.

The new Accelerator model changes that entirely. Instead of a fixed connector that requires recurring updates, the Accelerator is a delivered framework; a blueprint customers can build on and tailor to their specific needs. It is the foundation for integration between SAP and Akeneo, built upon SAP guided best practices, but now the control of configuration and customization lies where it belongs: in the hands of the customer.

Built on the SAP Business Technology Platform (BTP) and powered by Akeneo’s preconfigured iFlows, the SAP S/4HANA Accelerator helps customers and system integrators reduce complexity, speed up time-to-value, and scale with confidence. The result is deeper, more flexible connectivity between two highly configurable systems without the ongoing maintenance burden.

This new approach promotes agility and autonomy. Customers can configure integrations to match their SAP setup, extend them as their business evolves, and maintain them independently without waiting for product updates. 

Benefits of the Akeneo SAP S/4HANA Accelerator

The Akeneo SAP S/4HANA Accelerator delivers tangible advantages for enterprise teams looking to connect product and operational data seamlessly. From faster implementation to greater flexibility and scalability, it’s designed to simplify integration while empowering businesses to move at the speed of their markets. 

In particular, this new feature enables:

• Faster connection setup: The Accelerator provides a pre-built integration foundation that shortens implementation timelines and gets your systems talking to each other sooner.
• Greater flexibility: Every SAP environment is unique. The Accelerator allows customers to customize integrations for their particular data structures, business processes, and regional requirements.
• Improved governance: With a standardized framework and consistent data mapping, organizations can maintain a single source of truth across SAP and Akeneo, improving product data quality and governance.
• Lower maintenance: Because customers can make adjustments directly, there’s no need for recurring updates or dependency on vendor releases. The framework is stable, modular, and self-sustaining.
• Future scalability: As your business evolves, your data model will too. The Accelerator’s flexible architecture ensures it can scale alongside your product catalog, market expansion, or system evolution.
• Reduced cost of ownership: Built on SAP’s high-performance Integration Suite, the Accelerator provides a reliable blueprint for connecting SAP ERP and Akeneo PIM, reducing custom development, manual work, and maintenance costs.
• Faster time-to-market: The Accelerator automates the activation of ERP information into Akeneo PIM, so teams can quickly enrich product data and share it across all channels, accelerating the entire go-to-market process.

How the SAP S/4HANA Accelerator Works

At its core, the Akeneo SAP S/4HANA Accelerator enables bidirectional data flow between SAP ERP and Akeneo PIM. Product information moves seamlessly between the two systems using prebuilt iFlows that handle everything from data import and transformation to event-based synchronization.

From ERP to PIM

When product data originates in SAP, the Accelerator ensures it reaches Akeneo efficiently through three main stages:

1. Import batch data: Define how product data is collected and filtered from SAP S/4HANA, then send it to the orchestration flow for processing.
2. Message mapping: Transform SAP data fields into Akeneo’s format and structure so that product attributes, categories, and hierarchies align perfectly.
3. Batch orchestration: Manage the end-to-end process — authentication, data mapping, validation, and final delivery into Akeneo PIM — ensuring the right data arrives in the right place.

From PIM to ERP

Enriched product information can also flow back into SAP through event-based communication. These iFlows ensure near real-time synchronization so that updated product data in Akeneo is reflected in SAP automatically.

1. Create a subscriber to the Akeneo Event Platform: Generate a subscriber ID to receive events from Akeneo PIM.
2. Create a subscription: Select which events to subscribe to (for example, when product data is updated or published).
3. Akeneo PIM event listener: Capture events in a queue as they occur.
4. Akeneo PIM event consumer: Deliver these events back to SAP or another system of choice, ensuring synchronized and up-to-date product information across the enterprise.

The result is a robust, real-time data exchange that keeps every product record consistent, from the ERP backbone to the digital shelf.

Empowering the Next Era of Product Transparency

The Akeneo SAP S/4HANA Accelerator represents a significant step forward in unifying product information management with enterprise operations. It simplifies integration, accelerates deployment, and empowers customers to maintain control over their connections, all while laying the groundwork for a more transparent, data-driven future.

As global transparency standards like DPP take effect, businesses will need to ensure every product carries a complete, trustworthy data record; one that can be accessed by consumers, partners, and regulators alike. That means operational and marketing systems can no longer live in silos. They must work in concert to tell the full story of every product.

With the new Accelerator, SAP and Akeneo customers are equipped to do exactly that. By connecting the dots between supply chain and product experience data, organizations can ensure accuracy, traceability, and sustainability across every touchpoint.

To learn more about the Akeneo SAP S/4HANA Accelerator, you can register for our live Deminar on December 3 to see how you can create richer, more efficient product experiences with Akeneo and SAP today.

For years, B2B IT teams have had a reputation for moving toward the digital future at… well, let’s call it a “carefully considered pace.” And who can blame them? When you’re managing sprawling infrastructures, complex tech stacks, and the never-ending list of “critical priorities,” adopting new innovations can feel less like turning a corner and more like steering a cargo ship with a canoe paddle.

However, AI seems to be changing that stereotype. Nowadays nearly 30% of B2B decision-makers begin their research on AI platforms, and 78% of B2B organizations have implemented AI into at least one business functionality. Instead of inching toward transformation, many B2B organizations are suddenly finding themselves accelerating towards the digital future, sometimes by choice, sometimes by necessity.

With that in mind, let’s take a look at  why AI is giving B2B IT teams a long-overdue boost into the future, how it’s changing their day-to-day reality, and what forward-looking teams can do to stay ahead.

The Impact of AI on B2B IT Teams

AI’s growing influence across the B2B landscape is creating new pressures, new responsibilities, and new opportunities for IT leaders:

1. AI Helps IT Shift From Manual Automation to Intelligent, Adaptive Systems

Traditional workflows depend on inflexible logic: predefined triggers, fixed conditions, and carefully engineered sequences. 

AI changes that. Instead of building a thousand branches in a flowchart, IT can enable systems that continuously interpret context, predict needs, and determine the next best action automatically. 

When AI handles the repetitive, predictable aspects of workflows, IT gains the bandwidth to focus on innovation and higher-value initiatives; IT teams can shift from being reactive firefighters to proactive innovators. Instead of drowning in maintenance work, they can invest their time where it matters most: modernizing infrastructure, strengthening security posture, improving digital experiences, and exploring emerging technologies that could unlock new value for the business.

Organizations that operationalize AI in IT often report higher productivity, faster project delivery, and, perhaps most importantly, a renewed sense of purpose among their teams. When IT professionals are empowered to focus on solving business challenges rather than clearing backlogs, they’re able to contribute more strategically, partner more closely with the business, and drive initiatives that make a real impact.

2. AI Requires IT to Architect Infrastructure That Supports Real-Time Decision-Making

Whether it’s dynamic pricing, product search, personalization, or inventory forecasting, AI only performs well when it has access to reliable data in real time. That places enormous responsibility on IT teams to modernize the underlying architecture that hosts and syndicates product data. Legacy batch processes, overnight sync jobs, and sluggish APIs simply cannot support the expectations of agentic AI. Instead, IT must build environments where data flows continuously and updates propagate instantly across ERP, PIM, OMS, DAM, CDP, and other systems that fuel AI-driven decisions.

The challenge is both technical and organizational. IT needs to align data governance, system ownership, and update processes to guarantee every team contributes to a consistent and reliable flow of information.

The shift toward real-time decisioning also expands IT’s role in performance optimization. High-volume requests from AI agents create new strains on infrastructure. IT must evaluate caching strategies, compute scaling, cloud utilization, and network throughput to ensure AI operations don’t introduce friction or system instability, which leads us nicely to our next point.

3. AI Demands Seamless Interoperability Across the Entire Tech Stack

For an AI agent to retrieve pricing updates, access product content, initiate fulfillment steps, or trigger customer workflows, every underlying system must be fully interoperable. This places IT at the center of ensuring that the organization’s architecture is stitched together and deeply integrated. APIs must function consistently, authentication must work smoothly, and system dependencies must be managed intelligently. When AI calls for data or triggers an action, there can be no bottlenecks.

Interoperability becomes even more important as AI evolves beyond simple query-response patterns into autonomous orchestration. An AI agent might need to retrieve product specs from PIM, confirm stock availability via OMS, calculate delivery timelines through ERP, and adjust pricing dynamically based on a CDP data signal, all within milliseconds. IT ensures these systems can talk to each other without breaking or contradicting one another.

This need for interoperability also drives IT’s vendor strategy. Not every platform is built for AI enablement, and not every API performs equally under stress. IT must make decisions about which platforms integrate well enough to support AI at scale, which require middleware, and which need to be replaced. AI highlights integration problems that were previously invisible, and IT becomes responsible for solving them.

4. AI Relies on IT to Build Continuous Feedback Loops for Ongoing Learning

AI gets better when it can continuously learn, and IT plays a crucial role in enabling feedback loops that can continuously train AI models. IT teams are the ones responsible for creating the mechanisms that help AI understand what works, what doesn’t, and how to adjust its behavior. The value of AI compounds over time, and IT is responsible for ensuring those compounding effects actually occur.

This responsibility also extends to monitoring for bias and unintended behavior. Because AI transforms over time, IT must design guardrails that keep its learning aligned with business goals and compliance requirements.

5. AI Forces IT to Manage Tech Sprawl and Overlapping AI Capabilities

As vendors race to embed AI into their platforms, IT teams face a growing risk of redundant investments. One system offers AI search. Another offers AI content enrichment. Another offers AI recommendations. Without a strategic view, organizations quickly pay multiple times for similar capabilities. IT becomes responsible for evaluating where AI adds real value and where it overlaps.

Managing tech sprawl is also critical to maintaining performance and long-term scalability. Every new AI feature introduces additional compute demands and integration requirements. IT must prevent platforms from accumulating disconnected AI functions that inflate operating costs without improving outcomes. In this new era, tech consolidation is about both efficiency and survival.

6. AI Expands IT’s Responsibility for Data Quality and Data Readiness

In many B2B organizations, product data is spread across ERP, PIM, spreadsheets, vendor portals, legacy tools, and shared drives. AI magnifies the issues buried in these systems. Missing attributes, inaccurate dimensions, outdated certifications, or mismatched hierarchies directly undermine AI’s recommendations and predictions.

IT must define how information flows, who owns it, how frequently it updates, which systems are the sources of truth, and what validation rules prevent errors from contaminating downstream AI processes. AI thrives on consistency, and IT becomes the gatekeeper that enforces it. Good governance transforms data from a liability into an asset.

With AI relying so heavily on structured, consistent, and enriched product information, platforms like Akeneo PIM become foundational to successful AI adoption. Akeneo centralizes product data, enforces data governance rules, fills content gaps, and ensures every system receives complete, high-quality information. By giving IT a single source of truth, Akeneo PIM removes one of the biggest barriers to effective AI and empowers teams to deliver the accuracy and speed today’s AI-driven experiences demand.

7. AI Requires IT to Balance Innovation With Stability and Performance

AI accelerates the pace of innovation, but it also increases operational complexity. IT teams must support new models, new integrations, new data flows, and new compute requirements without destabilizing the systems the business relies on daily. Innovate too slowly, and the organization falls behind. Innovate too fast, and the infrastructure buckles!

Balancing both demands a strong architectural strategy and continuous monitoring. AI may be the catalyst for innovation, but IT ensures the organization remains functional and secure as capabilities expand. 

Where IT Goes From Here

As AI automates routine tasks, optimizes workflows, and enables real-time decision-making, IT teams are stepping into a new era where their work is more strategic, more collaborative, and more influential than ever before.

This shift comes with new responsibilities: architecting real-time data flows, ensuring interoperability across an increasingly complex tech stack, safeguarding data quality, and maintaining the stability and performance businesses depend on. But it also unlocks new opportunities for IT to drive innovation, accelerate digital transformation, and deliver smarter, more connected experiences for every team across the organization.

And at the center of all this progress is data; the clean, consistent, enriched information AI needs to function. When the data is right, AI can finally do what it promises, and IT teams can lead the business confidently into the future.

The B2B organizations that will thrive in this new era are preparing their data, modernizing their architecture, and empowering IT to build the digital backbone of tomorrow. With solid data foundations and a balance of innovation and stability, IT turns AI’s potential into meaningful business outcomes. The future of B2B commerce is intelligent, and IT is the team that will make that intelligence possible.

For several years now, the conversation about AI’s impact on commerce has been about how it’s on the horizon; how one day it would revolutionize the way we shop, sell, and engage with products.

Well, that day is here.

About 80% of eCommerce businesses already leverage AI solutions to enhance operations and customer experience, and nearly half of all consumers have used AI tools while shopping.

But AI can only be as great as the technology and data that powers it, and 85% of AI projects fail because of low-quality or inconsistent product data. Without the right foundation, even the smartest algorithm can stumble. Because even though AI gets all the attention, it’s the alignment of data, systems, and workflows that truly makes it effective.

For IT teams, that’s where the real challenge begins. The effectiveness of AI depends on the choices IT leaders and businesses make today, the systems they connect, and the flexibility they design into their stack. So before we talk about building an AI-optimized future, it’s worth asking: what does that foundation look like when engineered for long-term innovation.

What is a Tech Stack?

A tech stack is the collection of technologies, tools, and frameworks that work together to power a company’s digital ecosystem. It includes everything from the software and programming languages used to create applications to the systems that store and process data within the infrastructure. Think of it as the digital foundation that keeps products and operations running smoothly, enabling communication between systems, ensuring performance, and supporting the overall user experience. Each element in the stack plays a specific role, and together they define how efficiently a business can operate and evolve in an increasingly digital world.

The flexibility of your tech stack determines how quickly you can adapt to new technologies. In this context, the tech stack becomes the connective tissue that ties everything together, ensuring every tool and process works in harmony. 

The Challenges of Building an AI-Optimized Tech Stack

Building an AI-optimized tech stack is far more complex than simply integrating new tools. The goal is clear, but achieving it means overcoming a series of technical, structural, and cultural challenges that can slow even the most forward-thinking teams, such as:

• Fragmented data and legacy systems: Many organizations are still running on a mix of old and new technologies that don’t naturally “talk” to each other. Forrester links fragmented data to lost revenue, slower time-to-market, and higher return rates.
• Poor data quality and governance gaps: Inconsistent or incomplete records can lead to misleading insights; according to Gartner, poor data quality costs organizations an average of $12.9 million annually. 
• Scalability and infrastructure limitations: AI workloads require compute power, storage, and architecture capable of handling real-time processing. Many tech stacks weren’t designed for that kind of scalability, forcing IT teams to modernize infrastructure while keeping operations running.
• Integration complexity: Connecting AI engines, PIMs, APIs, analytics tools, and front-end platforms can turn into a web of dependencies. Without an API-first approach, each addition risks creating new silos instead of eliminating them.
• Cultural and cross-functional misalignment: Even the best tech can fail if teams don’t align around shared goals. Silos between IT, product, and business units slow down decision-making and limit the potential of AI initiatives before they mature.
  

Key Steps to Building an AI-Optimized Tech Stack 

Building an AI-optimized tech stack is about creating a connected, flexible foundation that can evolve as fast as the technology itself. Every component, from infrastructure to governance, must support agility, scalability, and collaboration. Let’s take a look at how IT and business leaders can design a future-ready ecosystem that fuels innovation, empowers teams, and turns AI potential into real business growth.

1. Design for Flexibility and Future Growth

The pace of innovation in AI and commerce is relentless, and  inflexibility has become a liability. A system built for adaptability enables organizations to transform without rebuilding from scratch, integrating new tools and scaling operations as demand changes. Flexibility is the foundation that lets a company pivot quickly and stay relevant in a rapidly shifting landscape.

Scalable, API-driven environments make it easier to adapt to new technologies, expand into new markets, and respond to customer expectations faster, which leads us nicely to our second step.

2. Adopt Open, API-First Systems

While closed, proprietary systems might once have provided control and simplicity, an API-first approach allows data to flow freely across systems and can help eliminate silos, accelerate automation, and enhance collaboration across the business. In fact, 82% of organizations have adopted an API-first approach (a 12% year-over-year increase), and 65% now generate revenue from API programs, showing that flexibility and connectivity are essential to scaling AI-powered commerce.

By enabling plug-and-play integration, API-first design gives IT teams the freedom to innovate without heavy coding or custom workarounds. Need to replace a legacy tool or integrate an emerging AI model? APIs streamline these transitions, reducing risk and allowing you to build a stack that seamlessly integrates today’s workflows with tomorrow’s intelligent, connected tools.

3. Centralize Product Data Management with PIM

If data is the fuel that powers AI, then Product Information Management (PIM) is the engine that runs it smoothly. For IT teams, a PIM acts as the single source of truth for product data. It’s the core system where raw information is structured and enriched before it’s distributed across channels. By consolidating product records into a central hub, teams can maintain smooth data synchronization between platforms and reduce redundancy.

This consistency is crucial because AI depends on high-quality data to function effectively. Poor or inconsistent information leads to broken recommendations and inaccurate search results, which ultimately leads to (you guessed it) frustrated customers! With a centralized PIM, businesses not only enhance operational efficiency but also empower AI systems to deliver better insights and customer experiences.

4. Implement Strong Data Governance

Poor data hygiene leads to duplicated records, inconsistent information, and compliance risks that can damage both trust and performance. Establishing clear governance rules makes sure that all data entering your systems meets defined standards for completeness and accuracy.

Governance also means accountability. With AI influencing more purchase decisions and customer interactions, businesses must ensure transparency about how data is used and how AI makes recommendations. 

Ultimately, good governance is about protecting data as well as empowering it. When data is well-managed, AI systems can operate faster and more effectively, and deliver insights you can act on with confidence.

5. Embed Continuous Maintenance and Optimization

An AI-optimized stack is a living system that needs ongoing attention, so this means regular audits, system updates, and performance reviews ensure that integrations remain secure and aligned with new technologies. This proactive approach reduces downtime and keeps operations running smoothly as AI models and digital tools evolve.

Optimization goes hand-in-hand with adaptability. As buyer behavior changes and new capabilities emerge, businesses that regularly enhance their stack can seize opportunities faster than competitors scrambling to catch up. The goal is continuous alignment, making sure every system, process, and dataset supports growth in an ever-evolving digital landscape. 

6. Champion Collaboration Between IT and Business Teams

A truly AI-optimized tech stack doesn’t belong to IT alone. When business, marketing, and technical teams operate in silos, improvements slow down and data loses value. In fact, employees waste up to 12 hours per week hunting down information, leading to as much as 30% of total revenue loss due to inefficiency and misalignment. 

But when these groups work together under a shared vision, technology becomes a catalyst for growth rather than a drain on resources. This alignment allows business leaders to articulate strategic goals while IT teams translate them into scalable, technical solutions. It also encourages open communication about challenges and performance metrics, ensuring AI initiatives deliver measurable outcomes.

When collaboration becomes cultural, every department understands its role in maintaining and optimizing the stack. Data becomes more accurate, and processes are more aligned. 

Building for Intelligence, Not Just Integration

Creating an AI-optimized tech stack is about building the right environment where intelligence can thrive. When data, systems, and teams work in sync, AI becomes a capability that transforms how businesses operate and grow.

For IT and business teams, the challenge is to move beyond implementation and focus on orchestration. Success depends on aligning strategy and architecture, so AI can deliver real value. Organizations that achieve this balance build ecosystems that accelerate transformation and results.

Choosing the right Product Information Management (PIM) solution has never been more important. With customers expecting accurate, compelling, and consistent product information across every channel, brands are investing heavily in tools that make their product experiences shine. But with so many platforms on the market, each offering different features, promises, and levels of support, it can be difficult to know which solution will truly empower your teams and deliver long-term value.

That’s where independent, data-driven analysis becomes invaluable. Released earlier this year by Info-Tech, SoftwareReviews’ head-to-head comparison offers one of the most comprehensive views into how two leading PIM solutions, Akeneo Product Cloud and Inriver PIM, perform in the real world. 

If you’re looking for clear, actionable, and unbiased insights, this year’s findings provide exactly that. Below, we break down the results into an approachable, easy-to-read guide that highlights where each vendor stands and what it means for organizations striving to deliver better product experiences.

The New PIM Reality: Usability and Flexibility Matter More Than Ever

Not long ago, PIM projects were often defined by customization, long deployments, and complex admin requirements. But in 2025, companies want the opposite:

• Shorter onboarding time
• Cleaner user interfaces
• More intuitive workflows
• Less dependency on IT

That preference shows up loudly in this year’s data, with Akeneo receiving a 90% satisfaction score in usability and intuitiveness compared to Inriver’s 70%. Users repeatedly report that Akeneo feels lighter, easier to navigate, and better suited to cross-team collaboration, which is a major advantage as merchandising, marketing, eCommerce, and digital teams all take a seat at the product-content table.

Ease of data integration is also a key differentiator. Akeneo’s stronger ratings here hint at what many brands already know from experience: connecting PIM into the broader ecosystem (eCommerce, DAM, ERP, CMS) is where much of the project’s true value is unlocked. When integration feels seamless rather than strenuous, teams can move faster, experiment more, and deliver richer product experiences with less friction.

Feature-Rich Is Good. Feature-Useful Is Better.

One of the most interesting takeaways from the report is how users evaluate features. While both platforms offer the foundational PIM capabilities brands expect, When it comes to product feature functionality, Akeneo received an overall satisfaction score of 84%, while Inriver PIM averaged 70%.

Akeneo particularly excelled in areas such as:

• Workflow & approval management
• Product data analytics
• Advanced search & filtering
• Omnichannel information delivery

What does this tell us? Companies want to learn from their product data. They want insights about completeness, consistency, accuracy, translation needs, and readiness for every channel. Akeneo’s approach to analytics, built purposely for merchandisers and marketers, seems to resonate with what teams actually need.

This trend continues in workflow and governance features. Strong scores for Akeneo reflect a platform that supports content operations without requiring overly rigid, IT-heavy configuration. 

Innovation Is An Expectation

To better understand a customer’s long-term relationship with a vendor, Info-Tech’s SoftwareReviews calculates an “Emotional Footprint” to quantify the emotional sentiment held by end users of the software based on their experience with the vendor. The Emotional Footprint assesses five key areas: strategy & innovation, service experience, product experience, negotiation & contract, and conflict resolution.

Overall, Akeneo outperformed Inriver in three of the five considerations for the Emotional Footprint, with a particular lead in the product experience category:

• Reliable: Akeneo +97 vs. Inriver +71
• Enables Productivity: +97 vs. +77
• Performance Enhancing: +89 vs. +71
• Security Protects: +84 vs. +76

One of the clearest signals in the Emotional Footprint scores is that users deeply value a vendor’s momentum. Akeneo earns notably higher marks for being “continually improving,” “inspiring,” and “helping users innovate.”

In other words, customers feel the platform is moving in the right direction — fast.

This lines up with what’s happening across the PIM market as a whole. AI-powered enrichment, automated product data quality checks, smarter workflows, and customer-influenced product content are becoming essential differentiators. Brands need a provider that’s not only building for where the market is today, but where it’s going.

What This Means If You’re Evaluating PIM in 2025

Stepping back from the individual scores, the report paints a bigger picture about where the PIM market is headed and what buyers are prioritizing as they plan for the next wave of digital transformation. Whether you’re replacing a legacy system, consolidating tools, or building a more modern product experience stack, the data offers several important insights about how teams are thinking today.

1. Teams want PIM platforms that feel like modern SaaS, not legacy enterprise tools.

PIM used to be synonymous with complexity: long implementation timelines, heavy customization, and interfaces that only a small group of specialists could navigate confidently. But that model no longer fits how companies operate. Today’s digital teams expect their PIM to feel as intuitive as the rest of their modern SaaS ecosystem. They need a system that anyone, from merchandising to eCommerce to marketing, can log into and understand without a training marathon.

2. Innovation is becoming a core purchasing criterion.

A stagnant PIM is a short-lived PIM.

There was a time when a stable feature set was enough to keep buyers satisfied; that era is gone. With AI reshaping everything from content creation to product recommendations to search experiences, and with customer expectations shifting faster than ever, businesses now need a PIM provider that keeps evolving.

Brands are making increasingly strategic PIM decisions, and they’re gravitating toward partners who demonstrate momentum, vision, and a track record of delivering meaningful enhancements.

3. Product experience is now the company experience.

One of the biggest mindset shifts in recent years is that product data is no longer viewed as a back-office asset but as a customer-facing one. The accuracy, richness, and completeness of product information now directly influence conversion, loyalty, return rates, SEO, and even the perceived quality of the brand.

In other words: the product experience is the customer experience.

Because of this, companies are placing greater emphasis on consistency and reliability. They need PIM platforms that support omnichannel delivery, make enrichment easy for non-technical teams, and offer analytics that surface gaps before they become customer problems. Platforms that deliver strong product experience capabilities ultimately help brands connect more confidently and authentically with their audiences.

4. Emotional sentiment strongly predicts long-term success.

Info-Tech’s SoftwareReviews report highlights something many tech buyers have long suspected but rarely measure: how a solution makes users feel matters just as much as what it can do. A positive vendor relationship typically correlates with smoother implementations, higher adoption, and greater ROI.

Emotional sentiment is especially important in PIM, where success depends on collaboration across merchandising, marketing, eCommerce, compliance, and IT. If a platform frustrates people or feels unnecessarily complex, adoption will lag and data quality will suffer. But if it feels supportive and empowering, teams are more likely to champion it, maintain data standards, and share ownership of product content, all of which form the foundation of a healthy product experience ecosystem.

The Bottom Line for PIM Buyers

So what can we take away from Info-Tech’s SoftwareReviews report? Companies are gravitating toward PIM solutions that combine strong functionality with strong usability and a strong vendor relationship. Akeneo’s leadership across innovation, product experience, emotional sentiment, and feature satisfaction suggests it’s hitting the mark for the needs of today’s product-driven organizations.

Inriver remains a respectable choice, with particular strengths that may matter to certain buyers. But when viewed through the lens of where the market is heading, toward flexibility, speed, and customer-influenced product experience, Akeneo’s momentum is hard to ignore.

If you’re choosing a PIM in 2025, the takeaway is simple: look beyond features, and evaluate how each platform helps your teams work smarter, collaborate better, and prepare for the future of product experience.

You can download the full head-to-head report today to explore all the findings and decide which PIM is right for your organization.

Each July (and this year, October), Amazon turns the internet into one big checkout line, and somehow, we all end up holding a cart. This year was no exception: Amazon announced that Prime Day 2025 was its biggest event ever, breaking records in sales and global reach. What began as a mid-summer promotion has evolved into a worldwide retail spectacle that defines how consumers shop and how competitors scramble to keep up. From flash deals to algorithm-fueled recommendations, it’s the ultimate test of how technology, timing, and trust converge to drive digital commerce.

Between July 8 and 11, part one of Amazon Prime Day this year, shoppers spent $24.1 billion online, a 30% increase over last year. And during the sequel, from Oct. 7–8, consumers spent $9.1 billion across US retailers.

Beyond the record numbers and lightning deals, what does Prime Day really tell us about where eCommerce is headed? What can retailers and shoppers learn from this year’s event? Let’s dive into the key takeaways that reveal how Amazon continues to redefine the retail landscape one Prime Day at a time.

6 Key Takeaways From Amazon Prime Day 

1. Voice Search and Conversational AI Are Transforming the Way We Shop

Prime Day made it clear that modern shopping is conversational. Voice search and AI-powered assistants gained significant attention as more consumers used Alexa and Rufus to ask questions and complete purchases without ever touching a screen. 

Roughly 20.5% of people worldwide now use voice search, with more than 8.4 billion voice assistants expected to be active across devices.

Amazon is doubling down on that future with Amazon Nova Sonic, its new model that merges speech understanding and generation into a single, unified system. This integrated approach allows developers to build natural, human-like conversational experiences that respond with remarkable speed and realism, setting a new industry standard for fast, scalable customer interactions.

For retailers, this shift means product data must evolve to reflect how people talk, not just how they type. Attributes, titles, and descriptions should mirror the way shoppers naturally phrase questions, whether it’s “What’s the best wireless speaker for travel?” or “Show me eco-friendly skincare.” Structured and conversationally aware product information — powered by solutions like Akeneo Product Cloud — ensures that every voice query connects customers to the right product, every time.

2. Augmented Reality Becomes the New Standard

What was once a novelty is now an expectation: Augmented Reality (AR) has become a standard part of how consumers explore and evaluate products online. Shoppers are no longer content to imagine how an item will look or fit; they want to see it in their own space, from their own perspective, before they buy. 

Amazon has continued to expand AR features such as “View in Your Room” and virtual try-ons for fashion, furniture, and beauty products, enabling customers to visualize items with impressive realism.

High-quality visuals, 3D models, and detailed metadata now form the foundation of a compelling AR experience. Platforms like Akeneo Product Information Management (PIM) help ensure that this data (from dimensions and materials to accurate color and texture details) is complete and consistent across channels. When paired with immersive technologies, that data transforms into interactive storytelling that builds trust and drives conversion. 

3. Consumers Spent Significant Time Comparing Products

The spending figures from Amazon Prime Day 2025 tell a compelling story: households were buying more, but also buying smarter. 51% of households placed two or more separate orders during the event, and 18% topped $200 in total spend. This pattern of multiple orders and elevated household spend suggests that while the urgency of the deal remains a factor, shoppers are now pacing their purchases and checking back to compare options before committing. This ultimately leads to more confident (and higher-value) decisions.

On Prime Day, even small details like clearer specifications, richer imagery, or transparent delivery information could determine whether a shopper stayed or bounced.

4. Automation Took Center Stage,  From Inventory to Dynamic Pricing

Behind every lightning deal and overnight delivery lies an invisible network of automation. And Prime Day showcased just how advanced that network has become. Sellers leaned heavily on AI-driven systems to forecast demand surges, monitor stock levels, and fine-tune pricing dynamically as trends changed throughout the event. An analysis of Amazon Prime Day 2025 emphasized that automation and AI were the backbone of the event’s success. It noted that Amazon’s vast logistics and supply-chain operations relied on predictive demand forecasting, real-time inventory management, and fulfillment automation to handle record order volumes efficiently. In short, AI-driven automation was powering Prime Day.

For Amazon, this sophistication translates into unprecedented efficiency. Algorithms constantly balance consumer demand and fulfillment capacity to keep both prices and availability optimized. For sellers, automation ensures that popular products stay in stock and are priced competitively without endless human oversight. It’s the ultimate balancing act: maximizing profit margins while delivering value at the speed of modern commerce.

But automation is also helping drive sustainability behind the scenes. By optimizing routes and inventory management, the same systems that accelerate fulfillment are also minimizing waste and improving energy efficiency, showing that innovation and environmental responsibility increasingly work hand in hand.

5. Marketplace Expansion Is Unlocking New Opportunities

And speaking of marketplaces, Amazon’s growth in 2025 made its momentum impossible to ignore. The launch of the Amazon Ireland store marked its 23rd marketplace worldwide, expanding access to millions of new customers and reinforcing Amazon’s dominance in Europe. For sellers, this expansion brings opportunity, but also complexity. Reaching new audiences means adapting to new languages, currencies, tax systems, and cultural expectations, all without losing brand consistency.

International expansion now demands a meticulous approach to product information. What resonates with a U.S. customer might not appeal to a shopper in Dublin or Düsseldorf. Localization is more than translation; it’s about context, tone, and cultural nuance. From product names to sizing charts (and even color descriptions!), every detail matters.

This is where structured data management becomes mission-critical. With tools like Akeneo PIM and Akeneo Activation, brands can syndicate accurate, localized content to every marketplace while maintaining a unified brand identity. 

6. Sustainability Is Becoming a Key Purchase Driver

Sustainability has become a deciding factor in how people shop. As consumers grow more environmentally conscious, programs like Amazon’s Climate Pledge Friendly have gained real traction. The initiative, which highlights products certified for meeting sustainability standards such as compact packaging, energy efficiency, or carbon-neutral production, continues to shape buyer preferences across categories. Shoppers are now scanning for that green badge before clicking “Add to Cart,” signaling that sustainability has moved from idealism to expectation.

According to Amazon, millions of products now carry the Climate Pledge Friendly label, and participating brands have seen measurable boosts in visibility and conversion. 

Transparency also translates directly into loyalty and spend: nearly 42% of consumers are willing to pay 24% more when a brand clearly communicates its values. Environmental impact and product lifecycle transparency are increasingly influencing where and how people spend their money.

For brands, meeting these expectations starts with accurate and trustworthy data. Solutions like Akeneo Supplier Data Management (SDM) and Akeneo PIM empower businesses to collect and enrich sustainability-related information (from material sourcing and packaging details to verified certifications) directly from their suppliers. Together, SDM and PIM create a single source of truth for sustainability data, enabling brands to communicate their environmental impact clearly and confidently across every channel. With this level of visibility and control, brands can back up their claims and meet marketplace requirements like Amazon’s Climate Pledge Friendly program.

The Commerce Lessons Behind Prime Day

Amazon Prime Day 2025 was another glimpse into the future of digital commerce. From conversational AI to automation, AR, and sustainability, every trend pointed to the same truth: data now drives every decision, interaction, and experience. And brands will thrive by treating product information as a strategic asset, one that powers discovery and conversion, rather than an afterthought.

With solutions like Akeneo Product Cloud, businesses can turn scattered data into actionable insight, ensuring every product, in every channel, tells a consistent and credible story. As commerce continues to change, the most successful retailers will anticipate them through better data and a deeper understanding of what their customers truly value.

Love it or hate it, AI has an impact on revenue.

PwC predicts that AI technology could generate $15.7 trillion in revenue by 2030, boosting the GDP of local economies by an additional 26%. And a study we conducted here at Akeneo found that two-fifths (40%) of consumers were willing to pay an average of 25% more for a more personalized, tailored experience powered by AI.

At peak moments, AI-assisted experiences are now moving the market at scale: during the 2024 holiday period, AI-influenced online sales reached $229B globally as shoppers interacted far more with chatbots and AI assistants. Amazon recently announced that it expects its AI shopping assistant, Rufus, to generate an extra $10 billion in sales this upcoming holiday season.

And of course, LLM-powered search and shopping is only continuing to grow in popularity as it slowly poaches searchers from more traditional methods; AI overviews can reduce click-throughs from traditional search results by anywhere from 15% to 64%, depending on the industry and search type, and 60% of searches now terminate without users clicking through to another site. 

 Now, it’s pretty obvious how this transition from traditional search and shopping experiences to AI-powered ones can affect customer-facing teams like marketing, eCommerce, and product teams: 56% of marketers say their company is taking an active role in implementing and using AI, and 73% say AI plays a role in creating personalized customer experiences.

But there’s a quieter, equally critical shift happening behind the scenes: IT departments are quickly becoming the gatekeepers of AI readiness.

The Impact of AI Commerce on IT Teams

In a commerce environment where speed and relevance drive conversions, poor data quality is a direct threat to revenue. Gartner estimates poor data quality costs organizations an average of $12.9 million per year, while Forrester finds that fragmented data directly contributes to lost revenue, slower time to market, and higher return rates.

As AI-powered search becomes standard, IT leaders are under mounting pressure to adapt their tech stacks to meet an entirely new set of requirements. 

Traditional automation is built on preprogrammed workflows, but in the age of intent-driven search and AI search agents, IT teams must design systems that allow AI agents to sense, decide, and act dynamically based on changing conditions, often without waiting for human approval.

This requires an architecture capable of supporting real-time data integration across core systems like ERP, PIM, OMS, DAM, and CDP, ensuring that AI tools have immediate access to accurate information. It also demands seamless interoperability, so AI can call APIs for pricing, content, or fulfillment data whenever needed, without bottlenecks. 

Just as importantly, these systems need feedback loops that enable AI agents to learn from results, refine their decision-making, and improve over time. Without these elements in place, AI simply can’t deliver on its promise of more intelligent, adaptive commerce experiences.

But meeting these requirements comes with challenges. IT leaders must avoid the costly trap of duplicate tech investments as AI capabilities spread across multiple platforms, and they must keep system sprawl and operating costs under control in order to maintain performance and manageability as stacks grow more complex. 

With MIT recently announcing that 95% of GenAI pilot programs fail and Gartner reporting that over 40% of current agentic projects will stall out before 2027, it’s become more important than ever before to ensure that these AI investments are successful, but the question still remains; how?

How IT Can Ensure AI Success

Here’s the harsh reality: while ERPs and MDMs play essential roles in managing operational and generalized data, they weren’t designed to handle the kind of rich, contextual product information that powers revenue and fuels AI.

This is where Product Information Management (PIM) comes in.

Think of PIM as the end-to-end supply chain for product data. Just as supply chains ensure raw materials get transformed into finished goods and delivered to customers, PIM ensures product information is enriched, organized, and distributed wherever it’s needed.

This matters because AI can only work with the information it’s given. A large language model or generative search tool isn’t magic; it can’t invent accurate specs or infer missing details. To interpret buyer intent and deliver relevant recommendations, these tools need structured, complete, and contextualized product data. 

For IT leaders, PIM directly addresses the core priorities of integration, governance, and scalability. 

Integration

On the integration front, modern PIM systems are built API-first, making it easy to connect with ERPs, MDMs, DAMs, eCommerce platforms, and increasingly AI engines. Instead of custom workarounds or brittle connectors, IT departments gain a flexible, future-proof hub that allows product data to flow where it’s needed with a PIM.

Governance

Governance is another critical advantage with PIM as organizations can establish clear workflows and validation rules to ensure that data is complete, accurate, and compliant with internal standards and external regulations. This reduces the risk of bad data reaching buyers (or worse, regulators) and builds the trust that’s essential when AI is making recommendations on a company’s behalf; as Gartner predicts, enterprises using AI governance platforms will enjoy significantly higher customer trust ratings in the years ahead.

Scalability

Finally, there’s scalability. AI-powered commerce is evolving quickly, and businesses need tech stacks that can adapt just as fast. A PIM system offers the agility to expand into new channels, markets, or business models without rebuilding the entire data infrastructure each time. Whether it’s supporting a new conversational AI interface, integrating with a marketplace, or enabling real-time product updates, PIM provides the flexible foundation IT leaders need to scale with confidence.

Ultimately, investing in a PIM gives IT leaders and their organizations the agility to adapt quickly, the governance to ensure accuracy and compliance, and the integration layer needed to connect every tool in the stack. Most importantly, it positions businesses to fully leverage AI commerce tools and deliver the kind of seamless, trustworthy product experiences that today’s buyers expect.

IT As the Gatekeepers for AI-Driven Revenue

The interface of commerce is shifting from search bars and storefronts to intelligent, adaptive systems that work on behalf of the customer.

At the heart of this transformation are IT teams. Marketing, product, and commerce leaders may shape the customer-facing experiences, but those experiences are only as strong as the infrastructure behind them. 

IT holds the keys to making AI commerce possible: ensuring real-time data flows, connecting systems through APIs, governing information for trust and compliance, and maintaining the agility to adapt as technologies evolve.

The path forward for IT leaders is clear: embrace openness, centralization, governance, and continuous evolution. By doing so, they will not only keep their organizations competitive but also help define what AI-powered commerce looks like in the years ahead. 

In case you missed it, we shared some very exciting news last week as part of our 2025 Autumn Release: the launch of Akeneo DAM!

For years, our customers have trusted Akeneo to help them manage, enrich, and distribute product information seamlessly across channels. Now, with Akeneo DAM, we’re taking that vision one step further, bringing digital asset management directly into the Akeneo Product Cloud.

But what exactly does Akeneo DAM do? And more importantly, how can it help your teams work smarter, faster, and more creatively? Let’s take a closer look.

Challenges of Asset Management

Today’s commerce teams face a growing list of pressures. There’s constant demand to do more with less; launching products faster, expanding to new channels, and creating content that converts, all while budgets tighten and headcount stays the same.

Digital assets are at the heart of those challenges. Images, videos, PDFs, 3D renders, and marketing visuals power every customer touchpoint, from product pages to ads to in-store displays. But managing them efficiently is another story.

Finding the right asset can feel like searching for a needle in a haystack, especially when assets are scattered across shared drives, cloud folders, and disconnected tools. Even once the right file is located, preparing it for each channel (resizing, cropping, adjusting backgrounds, creating locale-specific variations, etc.) can eat up hours of manual work.

At the same time, organizations are under pressure to prove ROI on every technology investment. Maintaining multiple systems and integrations drains IT resources, while the learning curve for new tools slows team adoption. Jumping between platforms to connect product data, digital assets, and marketing content is inefficient and frustrating.

And here’s the reality: today’s customers won’t wait. If product pages or visuals don’t load quickly, they move on. Teams need to deliver engaging, on-brand assets instantly, everywhere their customers shop, all while facing shrinking budgets and expanding channel requirements.

Introducing Akeneo DAM: One Seamless Workflow for Products and Assets

That’s where Akeneo DAM comes in. Built natively within the Akeneo Product Cloud, it gives you a single, unified workflow for managing both product information and digital assets without the headaches of multiple tools, integrations, or extra maintenance.

With Akeneo DAM, you can prepare and send assets to market alongside product information in one streamlined process. There’s no need for additional connectors, middleware, or external systems. Everything lives and works together harmoniously in one place.

Standalone DAMs offer robust, enterprise-level functionalities, and are probably the right solution for larger organizations with complex digital asset management needs. But Akeneo DAM offers a simple, straightforward solution for those organizations who want to associate digital assets directly with products already in Akeneo PIM, allowing their teams to manage and enrich both product data and digital assets in tandem. You can define relationships, assign attributes, localize for different locales or language variations, and ensure every image, video, and document supports the story your product tells.

Akeneo DAM also comes with advanced metadata management and AI-powered asset tagging that makes organizing and retrieving assets effortless. Each file is enriched with context and linked to product attributes, categories, and usage guidelines so that you can find what you need when you need it.

And don’t fret – if your organization already has a corporate DAM, Akeneo DAM can serve as the perfect bridge. Corporate DAMs are great for housing brand and campaign assets, but they often lack visibility into product details or channel-specific needs. Akeneo DAM fills that gap by connecting creative assets to the real-world context of your product catalog, order, and hierarchy.

In short, Akeneo DAM brings speed, consistency, and simplicity to how you manage and deliver digital assets so your teams can focus less on juggling systems and more on creating experiences that convert.

Akeneo DAM Feature Highlights

1. Automated AI Asset Tagging

Finding and using assets has never been easier. With AI-driven asset tagging, Akeneo DAM automatically applies consistent, descriptive keywords to every file in your library. That means your teams can quickly locate the right assets, ensure alignment across campaigns, and maintain a cohesive content library without hours of manual organization.

By improving asset visibility, you not only save time but also enhance searchability and SEO, helping customers discover your products faster through more accurate and relevant keywords.

Less time spent organizing means more time creating and launching beautiful, on-brand product experiences.

2. Automated AI Asset Transformation

Creating multiple versions of an image for different channels is typically a tedious, manual process, but with Automated AI Asset Transformations, Akeneo DAM can automatically resize, crop, and remove backgrounds from images, applying intelligent editing that adapts each asset to your defined channel requirements.

Teams can scale transformations effortlessly across entire product families using pre-defined operations. This ensures every visual fits perfectly, whether it’s a mobile app thumbnail, a high-resolution print image, or a marketplace listing photo.

The result? Consistency and market relevance at scale, all without leaving Akeneo.

3. Global Content Delivery Network

Speed matters, both for your team and your customers. Akeneo DAM’s Global CDN ensures that product assets are delivered faster and more reliably across every touchpoint. By leveraging a global network of local servers, assets load quickly no matter where your customers are, reducing bandwidth usage and bounce rates.

Even better, assets are delivered directly from Akeneo Product Cloud, so there’s no need for extra software, infrastructure, or middleware. That means lower costs, faster page load times, and a smoother path from creation to conversion.

It’s another way Akeneo DAM helps teams go from “ready” to “live” in record time.

The Power of Akeneo DAM + Akeneo PIM

When you combine Akeneo DAM with Akeneo PIM, you unlock a whole new level of efficiency and performance. Here’s how this powerful duo transforms your go-to-market process:

• Deliver compelling product stories: Great product experiences aren’t just about accurate specs. By pairing product information with high-quality, context-rich assets, brands can increase conversions and build trust with customers. Akeneo DAM helps teams deliver immersive product stories that blend data and visuals seamlessly.
• Get to market faster: Time is money, especially in retail. With both product data and assets managed in Akeneo, teams can prepare and launch products faster, eliminating manual steps between departments and systems.
• Boost efficiency and focus: Gone are the days of toggling between five different tools to get one product ready for launch. With everything in one place, teams work smarter, reduce duplication, and keep their creative energy focused where it matters most.
• Reduce total cost of ownership: By consolidating tools, you also reduce your total cost of ownership. There’s no need for extra IT resources, and no middleware to maintain. It’s one system, one workflow, and one source of truth.

The Future of Product Experiences Starts Here

In a world where every click, swipe, and scroll shapes how customers perceive your brand, the right product experience can make all the difference. That experience depends not only on accurate data but also on engaging visuals, and bringing those two elements together shouldn’t be complicated.

Akeneo DAM was built to simplify that process, helping teams to manage, connect, and deliver digital assets as part of a cohesive product story. By uniting product information and assets in one platform, Akeneo DAM helps teams work faster, collaborate smarter, and launch stronger without compromise.

So whether you’re looking to streamline workflows, cut costs, or simply get your products to market faster, Akeneo DAM is here to help you do it all beautifully, efficiently, and seamlessly.

Ready to see Akeneo DAM in action?

Visit our Autumn Release page to learn more, or register for our live Deminar on December 3 to discover how your team can create richer, more efficient product experiences today.

Manufacturing isn’t what it used to be. The factory floor has traded in clipboards and constant machine checks for smarter systems that never take a break. 

In fact, 48% of manufacturers now use AI-driven predictive maintenance to stop breakdowns before they occur.

Artificial Intelligence has become a powerful engine for progress, taking over the repetitive tasks humans were never eager to do. While the more obvious applications of AI tend to be in the customer-facing areas like marketing and eCommerce, it can also work in the background to quietly keep production lines moving and the manufacturing industry evolving at full speed.

What Is Artificial Intelligence In Manufacturing?

AI in manufacturing refers to the use of AI algorithms, machine learning, and other AI systems to support and enhance every stage of the manufacturing process. Unlike traditional automation, which follows rigid rules, AI can process large and complex streams of information, adapt in real time, and uncover insights that static systems miss.

At its core, AI in manufacturing is not about replacing the human element but about adding an intelligent layer to the systems already in place. AI models are built to recognize patterns and handle volumes of information far beyond the reach of traditional tools. The result is a manufacturing process that is more adaptive and resilient. 

Unlike older systems bound by rigid instructions, AI introduces flexibility and continuous learning that gives manufacturers the ability to stay strong as customer expectations and technologies continue to advance.

The Impact AI Has On the Manufacturing Industry

Increased Efficiency

AI is transforming efficiency in the manufacturing industry through predictive maintenance and automation that reduce downtime and streamline production lines. With predictive maintenance, AI algorithms track the health of machines in real time, detecting early warning signs of failure and allowing scheduled repairs before breakdowns occur. 

A great example of this is Siemens MindSphere, an industrial IoT and AI-powered platform that connects factory equipment, collects sensor data, and predicts failures before they happen. By recommending optimal repair schedules and monitoring asset performance, MindSphere helps manufacturers avoid costly disruptions while maintaining peak efficiency.

Yet efficiency in manufacturing isn’t limited to machine health alone. AI systems also automate repetitive tasks that once consumed valuable human time, freeing workers to concentrate on more strategic responsibilities. From adjusting equipment settings on the fly to optimizing processes, AI applications ensure every stage of the manufacturing process runs at peak performance. The outcome is a faster operation that can adapt to changing demands without missing a beat!

Improved Safety Standards

AI-driven monitoring systems can oversee factory environments in real time, spotting hazards like overheating machinery, chemical leaks, or unsafe behaviors. By alerting teams early, these systems prevent accidents before they escalate, keeping people and operations out of harm’s way.

IBM Maximo Visual Inspection, an AI-powered tool within the IBM Maximo Application Suite (MAS), is a good example of these improved safety standards in practice. It uses computer vision to detect and identify unsafe conditions on the factory floor. Paired with predictive analysis, solutions like this give manufacturers early warnings, strengthen compliance with safety standards, and create safer working environments.

Beyond immediate detection, AI models process data from sensors and historical incidents to predict risks and enforce safety standards. Whether it’s forecasting equipment fatigue or identifying hazardous conditions, AI applications reinforce a culture of safety that reduces liability and creates a more resilient workplace for everyone.

Creation of Digital Twins

Digital twin technology is the practice of creating a digital replica (or “twin”) of a physical asset, machine, or process. This virtual model is connected to real-world equipment through sensors and IoT devices, allowing it to mirror performance in real time. Because it continuously receives live data, the digital twin behaves just like the physical object it represents, making it a powerful tool for experimentation and insight.

With this approach, manufacturers can test scenarios and analyze data in a risk-free digital environment before applying changes to the real-world system. A factory could model how a new process would affect output or stress-test a machine virtually to uncover potential weaknesses in order to streamline the process and prevent costly mistakes that come with testing in live production.

Digital twins become even more powerful when paired with AI. While the twin provides the virtual model, AI algorithms predict outcomes and continuously refine simulations. This turns digital twins from static replicas into AI-driven systems that can optimize workflows and even suggest improvements for sustainability!

We can look to BMW’s iFactory strategy as an example of this concept in action, which uses digital twins enhanced with AI to design, test, and optimize production systems virtually before they’re rolled out on the shop floor. By creating a complete digital mirror of its factories, BMW can fine-tune workflows and ensure sustainability goals are met, all without disrupting real-world production.

Enhanced Design

When it comes to design, AI adds both intelligence and creativity to the process! AI systems sift through vast datasets (such as customer preferences and market trends) to suggest improvements for product designs. Instead of relying solely on intuition, engineers gain data-driven insights that accelerate innovation and reduce the risks of trial-and-error development.

A strong example of this in the manufacturing industry is Autodesk Generative Design, which uses AI algorithms to explore countless design possibilities based on performance goals and constraints, which enables manufacturers to quickly identify optimized designs that are stronger while using less material.

Beyond optimization, AI also speeds up the entire design process by transforming raw ideas into viable prototypes at record speed and enhancing functionality while leaving less room for waste! Ultimately, AI-driven design enhances human creativity and streamlines the process of transforming concepts into solutions that are ready for production more efficiently than ever.

Simplified Supply Chain and Inventory

The supply chain is one of the most complex aspects of the manufacturing industry, and AI is proving invaluable in making it more predictable and resilient. AI solutions powered by machine learning help manufacturers forecast demand with greater accuracy, allowing them to optimize inventory levels and avoid both shortages and overstock.

Beyond efficiency, AI introduces a new level of transparency. Manufacturers can now monitor every stage of the manufacturing process, ensuring compliance while identifying and addressing bottlenecks in real time. This is especially crucial as Digital Product Passport (DPP) legislation goes into effect in 2026 and requires an unprecedented level of supply chain transparency and collaboration.

A solution like Akeneo’s Supplier Data Manager (SDM) can be very helpful when it comes to collecting and optimizing product data collected throughout the supply chain. SDM replaces outdated, manual processes with AI-driven automation that streamlines supplier data exchange and enrichment processes. By using shared templates and flexible workflows, suppliers can onboard product data at scale while manufacturers are able to ensure accuracy and consistency before bringing the data into Akeneo PIM.

Quality Control

Quality control is one of the areas where AI is making the most immediate impact. Traditional inspection methods often rely on human oversight and manual checks, which can miss subtle flaws.

A great example here is LandingLens by Landing AI, which is a tool that uses computer vision to inspect products in real time and catch defects that might otherwise go unnoticed. By reducing recalls and improving consistency, tools like this help manufacturers deliver excellent products at scale and ensure their quality is reliable at every stage of the manufacturing process.

Sustainability

With nearly half of all consumers willing to pay 25% more if a company were to clearly communicate sustainability practices, it’s no wonder that more and more manufacturers and merchants are looking to collect, track, and communicate this information. 

From reducing material waste to lowering energy consumption, AI-driven systems improve production schedules and resource use in ways that lessen environmental impact without sacrificing performance. A strong example is Microsoft AI for Sustainability, which provides real-time insights into energy use and emissions, enabling manufacturers to track their footprint and take action.

The benefits go beyond compliance. AI enables manufacturing processes that are both efficient and responsible, reducing costs while aligning with regulations and consumer expectations for greener practices. Embedding AI into daily operations allows manufacturers to position themselves as both innovative and responsible, proving that progress and sustainability can go hand in hand.

AI Is Redefining Manufacturing

From predictive maintenance and digital twins to supply chain resilience, AI is reshaping every corner of the manufacturing process. And with platforms like SDM, manufacturers can extend that intelligence beyond the factory floor into their product data, ensuring efficiency and accuracy.

The future of manufacturing lies in the hands of companies that embrace AI as a strategic partner. Those who invest today will be best positioned to deliver consistent product experiences and lead confidently into the next era of commerce.

Shopping today feels less like a straight line and more like a maze, one that consumers navigate with a tap, a scroll, and a swipe. They jump between apps, stores, and social feeds without missing a beat, expecting every stop along the way to feel connected and effortless. Their expectations are rewriting the rules of engagement for modern commerce.

According to our recent B2C survey, general and specialty retail stores and online marketplace websites emerged as the most common shopping channels. Even after years of eCommerce acceleration, shoppers haven’t abandoned the thrill of in-store experiences. The chance to see, touch, and test products still matters! Rather than choosing one over the other, consumers are blending both worlds, creating a commerce landscape where digital discovery and physical connection go hand in hand.

For businesses, the challenge is keeping up with this rhythm while maintaining consistency and speed across every touchpoint. Data, technology, and teamwork have become the glue holding it all together. 

Let’s take a closer look at the omnichannel trends redefining how consumers shop and how businesses sell, and how these changes are setting the pace for growth in 2025 and beyond.

What Is Omnichannel Commerce?

Omnichannel commerce means selling products across multiple channels while maintaining a unified customer experience. It brings together every touchpoint where customers interact with a brand, merging online platforms and physical stores into a single strategy. At its core, it’s about creating a seamless shopping journey that delivers personalization and consistent interaction at every stage.

8 Key Omnichannel Shopping Trends To Learn From 

If you want to understand where retail is headed, don’t just ask the brands, ask the shoppers. Consumers are already rewriting the rules of omnichannel retail, blending online, offline, and everything in between into their own dynamic shopping journeys.

From the channels they trust to the frustrations they voice, these trends reveal exactly what today’s buyers expect, and where retail businesses need to step up:

1. Customers Expect AI to Make Shopping More Personal

Shoppers today expect brands to know them, not just recognize them. Generative AI is transforming how customers discover and engage with products across channels. Consumers now rely on AI-powered tools for personalized recommendations, chatbot-assisted shopping, and dynamic search that surfaces the most relevant results. For many, it’s no longer about scrolling through endless product listings but instead letting an AI assistant handle the search and surface the right products that match their taste and budget instantly. In fact, 49% of global shoppers say AI-powered recommendations have directly influenced their purchase decisions, proving just how deeply these technologies now shape buying behavior.

For businesses, this transition represents a turning point in digital engagement. Generative AI helps brands create content at scale, from automated product descriptions to localized campaigns that resonate across regions. Retailers use AI to refine their personalization strategies and even predict purchasing behavior. But success depends on the quality of product data that fuels these algorithms: clean, complete, and clear information ensures that what AI delivers feels human and accurate. The future of omnichannel personalization lies in data precision and agility, where every recommendation reflects a brand’s understanding of its customer.

2. Shoppers Blend Convenience and Connection Through BOPIS & BORIS

Customers have embraced the best of both worlds. They want the convenience of digital shopping and the immediacy of physical pickup; and increasingly, they expect brands to offer both without compromise. “Buy Online, Pick Up In-Store” (BOPIS) and “Buy Online, Return In-Store” (BORIS) have become fundamental behaviors for modern shoppers. They like being able to browse from anywhere, secure an item in minutes, and either pick it up the same day or return it easily in person.

From a customer perspective, these options remove uncertainty and reduce waiting time. They make online shopping feel tangible and trustworthy. Shoppers also value the sense of control, being able to see and touch the product before finalizing their purchase or refund gives reassurance that pure eCommerce can’t match!

For retailers, however, this convenience comes with new operational complexity. Synchronizing product availability, pricing, and descriptions across both online and in-store systems is notoriously difficult. One mismatch can quickly lead to frustration and lost trust.

Akeneo Activation helps brands bridge the gap between systems and channels by automating the flow of accurate product information in real time. It eliminates manual work by keeping channel requirements up to date and ensuring that every product listing meets the unique specifications of each marketplace or retailer. With Activation, teams can launch new products faster, optimize live product detail pages (PDPs), and maintain cohesive visuals and descriptions — so every touchpoint delivers a unified experience for the customer!

3. Shoppers Research Online Before Buying In-Store

Consumers have become meticulous researchers. The Research Online, Buy Offline (ROBO) trend is proof that digital and physical shopping complement each other. In fact, the trend is stronger than ever, with up to 76% of consumers using multiple channels and touchpoints to inform their purchase decisions. Shoppers rely on online research to guide offline buying, checking specs, reading reviews, and watching influencer demos before ever setting foot in a store. They arrive ready to purchase instead of browse, turning in-person visits into the final step of an already digital decision.

For businesses, this behavior demands strong online content and absolute consistency across touchpoints. A detailed, genuine digital presence builds confidence and drives real-world conversions. Retailers that treat their online channels as digital showrooms (providing clear imagery and precise information) strengthen both their eCommerce and physical store performance.

4. Consumers Use AI Search to Find and Commit Faster

The rise of conversational search is transforming how shoppers discover and evaluate products. Consumers are no longer typing stiff keywords but are now asking natural, open-ended questions like “What’s the best cruelty-free moisturizer under $30?” or “Which headphones are most comfortable for long flights?” and expecting correct, personalized results instantly. It highlights a growing expectation for search tools that can interpret meaning and context, rather than simply respond to literal keywords.

AI-powered search represents a major leap from traditional keyword matching to intent-based discovery. Using machine learning and natural language processing (NLP), these systems can interpret what a shopper means, not just what they say. Rather than relying solely on static tags or product rules, AI search learns from user behavior and adapts accordingly. For example, if someone searches “best trail running shoes for wet conditions,” a standard search engine might list every product tagged “trail” or “running,” leaving shoppers to filter through irrelevant results. An AI-powered search, on the other hand, analyzes reviews, purchase data, and location trends to surface waterproof running shoes that perform well in rainy climates. It looks for products that truly align with the user’s intent!

As AI-driven commerce evolves, visibility now depends on how well products are understood by intelligent search systems. AI Discovery Optimization, a recent capability within Akeneo PX Insights, helps brands analyze how their products are discovered — or missed — across AI-powered shopping experiences. It surfaces insights on natural language and AI-assisted search behavior, highlights gaps in visibility and relevance, and identifies opportunities to enrich product data so it better reflects evolving consumer intent. By optimizing product content for emerging AI discovery channels, businesses can ensure they stand out with precision and clarity at every key point in the shopping journey.

5. B2B Buyers Mirror Consumer Shopping Behaviors

B2B buyers are no longer satisfied with outdated portals or clunky catalog PDFs. They expect the same simplicity and agility they experience as consumers, complete with personalized pricing and omnichannel consistency. In fact, 84% of B2B buyers say it’s important for sellers to provide a seamless experience across multiple channels, and 73% already prefer digital channels such as marketplaces and mobile apps as part of their purchasing process. This evolution shows that B2B commerce is no longer trailing behind B2C: it’s catching up fast.

For B2B sellers, this shift means elevating the buying experience to meet rising expectations. That involves managing multiple catalogs and configurations while ensuring that every piece of product information remains synchronized across systems and channels. Akeneo Shared Catalogs provides the foundation for this transformation by simplifying how product information is shared and managed. It enables businesses to customize and securely distribute up-to-date product data directly through a centralized online portal. Automatic synchronization with Akeneo PIM ensures that every catalog remains current and consistent, giving distributors, partners, and sales teams instant access to the information they need — without manual updates or IT involvement!

For B2B sellers, this means more than faster launches. Akeneo Shared Catalogs enables seamless collaboration and strengthens trust across partner networks. With Shared Catalogs, every stakeholder can confidently access the latest product details, pricing, and assets in real time, removing inconsistencies and minimizing inaccuracies. The result is a more transparent B2B buying experience, one that deepens relationships and transforms reliable product data into a true competitive advantage.

6. Shoppers Want Flexible Returns Without the Friction

Online shoppers have grown accustomed to flexible return policies, and they’re not giving them up anytime soon. But while customers love the convenience, returns are increasingly cutting into profits. In many cases, they’re the direct result of (you might have guessed it!) poor product data. Globally, around 40–50% of consumers returned a product in the past year due to incorrect or misleading product information. Returns create operational headaches for retailers and frustration for customers, often stemming from fragmented product data across channels.

Customer expectations around returns have never been higher. 38% expect free delivery, 33% expect a free return process, and another 28% prioritize an easy return experience. These expectations have become essential parts of what customers consider good service, not optional perks. Retailers that fail to meet them risk damaging loyalty and driving shoppers to competitors who make convenience a core part of their promise.

To adapt, businesses are turning to AI-powered tools and richer product content to reduce return rates and rebuild buyer confidence. By using AI to effortlessly generate, enrich, and translate product information, tailored to their brand voice, retailers can create more accurate and complete product experiences. Akeneo PIM makes this possible by centralizing and enriching product data so every channel displays up-to-date information. When product details are clear, shoppers know exactly what to expect, which leads to fewer surprises and fewer returns!

7. Traditional Search Still Matters in an AI-Driven World

Despite the rise of AI-powered discovery, traditional search remains a cornerstone of how consumers find products online. When it comes to online search and discovery, 26% of consumers say they’re most likely to use traditional search engines, while 22% turn to online marketplaces. These channels continue to dominate the early stages of the shopping journey, serving as the starting point for product research and validation.

For brands, this means that SEO and structured product data are still essential parts of an omnichannel strategy. Optimizing for both traditional and AI-driven discovery ensures that products appear wherever consumers begin their search, whether that’s Google, Amazon, or a generative AI assistant. The brands that maintain visibility across both established and emerging discovery channels are the ones that stay top of mind and top of search results.

8. Gen Z Is Defining What Omnichannel Really Means

Gen Z’s habits and demands are powering the transformation of the retail sector. As digital natives, they move fluidly across apps, devices, and physical stores without as if it’s second nature. They might discover a product on TikTok, compare prices on Google, check reviews on Reddit, and then complete their purchase in-store or through a mobile app. According to recent data, 36 % of Gen Z shoppers regularly use both online and in-person channels, making them the most omnichannel generation yet. For these shoppers, efficiency and authenticity are inseparable. They expect real-time updates and experiences they can trust, no matter where they shop.

For brands, this generation presents both a challenge and an opportunity. Gen Z demands transparency and speed. They are quick to abandon brands that deliver inconsistent information or clunky checkout experiences. To capture their attention, businesses must rethink omnichannel strategy not as a series of disconnected channels but as one continuous ecosystem — one that mirrors how young consumers actually evaluate and buy. That means ensuring every channel, from social commerce to brick-and-mortar, tells the same product story with the same level of clarity and appeal.

To achieve that level of consistency, brands need visibility into how their product experiences perform. This is where Akeneo PX Insights transforms complexity into insight. By measuring how product experiences perform across channels, PX Insights helps businesses understand where engagement thrives and where it falls short. It uncovers inconsistencies in descriptions or imagery, and pinpoints which content truly drives conversions among Gen Z audiences. Armed with these insights, brands can strengthen their omnichannel presence, ensuring every touchpoint feels trustworthy and true to the brand.

Turning Trends Into Opportunity

When you look at these trends together, they reveal one clear truth that experience transforms omnichannel strategy into a real opportunity. The future of retail is about weaving multiple channels into one seamless experience. Customers have made it clear: they want flexibility, reliability, and trust at every stage of their shopping journey. Brands that embrace a true omnichannel mindset, powered by accurate product data and AI-driven insights, will not only meet these expectations but turn them into lasting relationships.

In omnichannel retail, experience shouldn’t be an afterthought but the main priority. After all, 80% of consumers say the experience a company provides is just as important as its products or services, a clear reminder that experience is part of the product. When product information stays aligned across every touchpoint and the buying journey flows without barriers, what once felt fragmented turns into an opportunity. Every experience becomes a reason for customers to return.